Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity

Edward Sun, Timm Kruse and Yi-Ting Chen
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Timm Kruse: InfoTech
Yi-Ting Chen: College of Computer Science, National Chiao Tung University

Annals of Operations Research, 2019, vol. 281, issue 1, No 15, 315-347

Abstract: Abstract Regulatory reform enacted (e.g., the Dodd-Frank Act enforced in the U.S.) requires the financial service industry to consider the “reasonably expected near term demand” (i.e., RENTD) in trading. To manage the price impact and transaction cost associated with orders submitted to an order driven market, market makers or specialists must determine their trading styles (aggressive, neutral, or passive) based on the market liquidity in response to RENTD, particularly for trading a large quantity of some financial instrument. In this article we introduce a model considering different trading styles to satisfy the predictive near-term customer demand of market liquidity in order to find an optimal order submission strategy based on different market situations. We show some analytical properties and numerical performances of our model in search of optimal solutions. We evaluate the performances of our model with simulations run over a set of experiments in comparison with two alternative strategies. Our results suggest that the proposed model illustrates superiority in performance.

Keywords: Artificial intelligence; Algorithmic trading; Decision analytics; Discrete optimization; FinTech; Liquidity (search for similar items in EconPapers)
JEL-codes: C61 C63 G10 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-019-03150-0

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